Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.